Research Related to Autonomous Vehicles

Ever since the beginning of the Information Age, job automation has been gradually working its way into the everyday workplace. Job automation is the replacement of jobs traditionally done by humans by automatic systems, namely computers or robots. It is most commonly seen in industrial fields where the introduction of automation has massively improved speed and efficiency in factories, however, it generally comes with a significant loss in jobs, especially in more developed countries. According to Christopher Pissarides, depending on a country’s level of development, machines could replace anywhere from 3 to 14 percent of jobs by the year 2030. A significant player in this job loss could be the introduction of autonomous vehicles to the workforce, replacing most of the 1.7 million trucking and transportation jobs in America(Freedman). Autonomous vehicles (AVs), more commonly known as self-driving cars, vehicles that are able to drive themselves without human intervention.

By most definitions, the level of autonomy is graded in “levels” from zero to five, with a level zero vehicle lacking any automation and a level five being fully autonomous in all situations (Schoettle). Currently, level two autonomy is the only relatively common form of automation in production cars, with most car companies having some form of Automated Driving Assistance Systems (ADAS) in their vehicle models (O’Toole). These features are typically situational convenience or safety features such as “parallel park assist” or “automatic braking systems”. The development of automated vehicles is progressing extremely rapidly, however, as the first “level 3” production vehicle is set to be released by Audi in 2019, with Toyota and BMW set to release similar products in the two years following. AV technology has been evolving extremely rapidly in the past decade, but the idea of driverless vehicles has been around for nearly a century.

The first example of an AV was Francis Houndina’s “radio controlled vehicle”, which he demonstrated in 1925. The idea was not reintroduced in any major way, until the Defense Advanced Research Projects Agency (DARPA) announced its “Grand Challenge” in 2002 for researchers to build an autonomous vehicle that could navigate its way through the Mojave Desert. When the competition began in 2004, the “winning” entry set on fire after multiple hours and just eight miles of driving. Despite the failure, research has gained remarkable traction in recent years with millions of miles being driven by AVs by leaders in the industry such as Alphabet owned Waymo, Uber, and the GM owned Cruise (Dormehl). While partially for the benefit of technological advancement, there are a variety of practical reasons for the implementation of AVs. The largest and most obvious of these reasons is the potential to save lives by the reduction of traffic accidents.

Over 37,000 traffic deaths occur each year in the United States, with over 90% of them being attributed to human choice or error (Singer). Even if the best case scenario for AVs were a 10% reduction in crash rates, the potential to save countless lives in the long term makes the development of AVs priceless. On top of saving lives, the development of AVs will result in a sizable reduction of energy consumption through techniques such as “eco-driving” and “platooning” that are impossible for human drivers (Wadud). Mobility is also a huge boon to the development of AVs, as they could grand newfound ability for the disabled, physically impaired, and elderly to safely transport without the assistance of others. As a result of these numerous advantages over traditional vehicles and other forms of transportation, the implementation of AVs is inevitable, and an effort should be made to speed their implementation rather than wait for “perfect” vehicles.

With the majority of traffic accidents caused by human error, driverless vehicles are bound to improve safety for all users of the road. Due to the lack of long-term implementation of AVs on public roads, exact figures on the effectiveness of autonomous technology compared to human drivers do not exist. Nevertheless, the National Highway Traffic Safety Administration (NHTSA) stated that the introduction of Tesla “Autopilot” systems in their Model S and X vehicles reduced crash rates by 40% (Hawkings), pointing out that current technology is heading in the right direction. The speed of implementation for AVs will also be a major factor in saving lives. According to research done by the RAND Corporation, by beginning to implement AVs when they are 10% more safe than human drivers could save up to 200,000 lives in the time that it would take for AVs to become 90% safer than human drivers. Actual numbers may vary depending on the date of implementation, the extent of usage, along with the amount of unpredictable non-AV fatality rates.

For example, fatality rates were steadily decreasing from the 1950s onward, yet, the last decade has seen a plateau, followed by a 5.4% increase in fatalities from 2015-16. Regardless of the numerous variables, essentially every simulation run in the RAND study resulted in significantly more lives saved in the long term by immediately implementing AVs at a 10% increase in efficiency (Kalra). Even so, the general population may be understandably resistant to the implementation of AVs at a 10% increase as personal control is exchanged for the “greater good’ (Bonnefon). Furthermore, many studies have found that there is almost no tolerance for errors made by machine systems in comparison to their human counterparts. While it is important to compromise with public outcry, waiting for nearly perfect driverless technology could cost the deaths of thousands in motor vehicle accidents.

Playing an equally important role in the implementation of AVs is the increased efficiency these vehicles would grant. Congestion in massive urban areas such as Seattle (where only 11% of vehicles in the city are in use at any given time) would be heavily reduced. Small gains in efficiency could also be gained through minor applications such as errand running or use of transit times for productive activities, however, major applications may include the automation of semi trucks, ride sharing, and granting extra mobility to the elderly, disabled, and children (Alessandrini). Commercial use of this technology would result in noticeable economic gain, through the potential reduction of freight cost and time (Freedman). According to a study by the Earth Institute at Columbia University, ride sharing encouraged by the introduction of AVs in urban areas could reduce the amount of cars on the road by up to 50% (Radwha).

By reducing this many vehicles on highways, the mitigated congestion would result in a nearly 3% increase in fuel efficiency (Wadud). Even without accounting for potential increases in ride sharing, AVs are able to engage in many actions that are impossible for human drivers that could help aid in the reduction of over congestion and fuel emissions. One such ability is being able to safely drive much closer in proximity to each other than human drivers (O’Toole). This ability could also be used in “platooning”, or the coordinated use of close driving to increase aerodynamic efficiency. Energy use on light weight vehicles engaging in platooning could decrease anywhere from 3-25% on the freeway, depending on the car’s location in the “platoon”. Gains for semi trucks are just as substantial, reducing energy use from 10-25%. Another potential technique that could be used by AVs is “eco-driving” or the operation of a vehicle engine at its most efficient points. The resulting effects on emissions through eco-driving vary wildly with the situation, however, a noticeable reduction would likely take place over time (Wadud).

In addition to these techniques, AVs would likely have less “downtime” in their operation due to their more efficient navigation in situations such as parking. Combining with the AV exclusive techniques, most automated vehicles would be lighter and more fuel efficient vehicles, further contributing to the reduction of fuel related emissions (Alessandrini). The implementation of AVs would immediately significant reductions in vehicle emissions and congestion.One of the many advantages of AVs over other forms of “modern” public transport is its widespread availability. A common proposed alternative to the development of AVs in urban areas is the expansion of public transport, however AVs hold many advantages over these methods. In comparison to services such as buses or rail lines, vehicles in general are much more flexible in where and when they go. This is especially important in areas that are not incredibly urbanized, where public transport is virtually nonexistent. In comparison, more than 95% of US households have access to a personal transport vehicle.

Of those who do not own a vehicle, nearly a quarter of them still transport to work in employer borrowed vehicles. Public transit also suffers from a massive upfront cost. In 2012 alone, taxpayers spent $24 Billion on public transit services. In addition, transit receives three dollars in government subsidies for each dollar spent in fares. In 2012, only five percent of urban workers rode transit to their jobs. In addition, the average urban resident rides the transit an average of 44 times per year. Regardless, proponents of public transit push the lower per mile cost. In 2012, transit fares averaged out to $0.25 per mile compared to the $0.37 per mile of personal vehicles. However, after taking into account ride sharing between two or more people, personal cost drops significantly below that of transit.

Finally, advocates of public transit cite the large increase in safety that public transit provides as an advantage over traditional vehicles (O’Toole). In 2016, commuters reduced their crash risk by 90% in comparison to personal vehicles (Williams). However, those numbers are comparable to the likely reductions made by AVs in the future (Filler). Due to its lack of significant use, large upfront cost, and the objective inconvenience, support for public transit should instead be shifted to support of AVs in urban areas as the transportation of the future. An additional proposed alternative to widespread AV use are “smart highways”. Smart highways are roadways that contain terminals capable of relaying information to and/or guiding automotive vehicles. In order for vehicles to use this technology, they would require hardware that would allow them to use vehicle-to-infrastructure (V2I) features. Proponents of this technology state that the addition of these features, along with vehicle-to-vehicle communication (V2V) could remove the need for fully autonomous sensors and other hardware, as all driving tasks would be controlled by the smart highways themselves. In addition, all vehicles in an area would instantly respond to the actions of other vehicles through V2V technology. This idea comes with a plethora of issues, however, namely the increased susceptibility to abuse and hacking.

In a smart highway using this technology, a security breach any piece of infrastructure manipulate entire roadways and all the vehicles present on it. This large increase in risk comes with essentially no benefit, as in most cases, ADAS systems are just as effective as V2V and V2I systems. Smart highways also come with the requirement of constant and expensive government maintenance, which is unlikely to be present in all cases, as seen in the poor conditions of many current roadways. This also limits autonomous driving to roadways equipped with this infrastructure, which severely limits large scale improvements in driving safety. Smart highways pose many risks for almost no benefit over AVs, making them a poor alternative improving roadway safety. (O’Toole)The largest blockade to the implementation of driverless vehicles are the recent deaths occurring involving AVs. The first fatal crash involving a driverless vehicle occurred on May 7, 2016 when a Tesla Model S operating in “Autopilot” attempted to drive under the bottom of a semi trailer (Levin).

Tesla responded to the crash in an open letter which stated, “This is the first known fatality in just over 130 million miles where Autopilot was activated. Among all vehicles in the US, there is a fatality every 94 million miles.”. Additionally, at the time of the crash, the driver was distracted by watching a movie despite the “Autopilot” system explicitly stating that the driver’s attention must be focused on the road at all times while the system is in operation (Levin). More recently, the first pedestrian death occurred on March 18, 2018 when a self-driving Uber hit a pedestrian at 10 pm while walking her bike across the street in Tempe, Arizona. The pedestrian crossed the middle of the poorly lit road while wearing a black coat without making an attempt to avoid or acknowledge the vehicle. With these conditions, it is incredibly unlikely that any human driver would have been able to do a better job in avoiding the collision (Griggs).

Even disregarding this, national averages suggest that over 100 human deaths occurred on the same day, and four deaths occurred in the same hour (IIHS). Despite these factors, Arizona legislature has indefinitely suspended Uber’s ability to test self-driving vehicles in the state. By limiting the opportunities for leaders in the industry to test AVs, the pace of development drastically slows simply to ease invalid concerns. Due to AVs numerous advantages over traditional vehicles, it is inevitable that they will eventually replace human drivers. Furthermore, by waiting to implement these vehicles until they are nearly perfect verses gradually implementing as technology improves will cost thousands of lives. Along with this, AVs will be a significant help in decreasing congestion in heavily populated areas, decreasing fuel emissions, and providing extra mobility to the physically impaired. As safety technologies continue to improve, AVs will further grow its personal appeal and cost efficiency over forms of public transit. It is unavoidable that driverless technology will hit many costly road bumps on its way to implementation, however, the costs of waiting are far greater.

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Developing Information Technology Standards

Information technology (IT) plays an important role in economic growth, and social development, it is changing work and the world. Consider the invention of internet, the mobile phone, home wireless networks, computer algorithms that recognize faces, or self-driving vehicles. High speed internet connectivity has been available for decades, the distribution of high-speed internet connectivity to all over the world is still in the making. Technology for self-driving was recently implement but quickly removed of the market because one the car crashed and killed a passenger. Information technology (IT) standards are sets of specification for communication or performing actions that ensure that various technologies or products that implement certain specifications are compatible (e.g. David and Greenstein 1990; Lyytinen et al. 2008; Nicerson and zur Muehlen 2006; Weitzel et al. 2006).

IT standards are central to the integration of heterogeneous IS within organizations and thereby reduce the cost of managing IT infrastructure (Chen and Forman 2006). Both software development and the hardware involved in the IT industry include everything from computer systems, to the design, implementation, study and development of IT and management systems. The IT sector has emerged as a major global source of both growth and employment. Economies for the Information technology industry are high and unlike other common industries, the IT industry is knowledge-based.

The rate of diffusion of technology has influenced many forces, including demand, cost, technology maturity, competitive pressures, societal acceptance, government policies and regulations, safety requirements, resistance by entrenched interests, and the interventions of entrepreneurs in creating and marketing products. Currently the widespread use of computers, is digital and online data. Moving services and data to computers and online is what is referred as digitization, this changed and affected lives. Majority of people now use retail services such as Amazon, navigation services in cars and smartphones and free video internet calls. Businesses have been revolutionized by new computer systems that capture, organize, optimize, and program business processes. Healthcare is also changing using new computer technologies that will enhance the efficiency and quality of health-care delivery.

Education has also been impacted by digitization, with an increase of students taking online courses that include video lectures, and instructors giving instructions and answering questions through online discussion boards. Digitizing has creating opportunity for some workers to work at home using video conferencing and online business processes. Most jobs today involve in some communication with IT systems, that most of the workforce are informed and trained to use all the systems. Employees as well will encounter problems with information technology, and everyone depends on the systems to work perfectly for any task to be completed.

American corporations have spent billions of dollars on digitalization operations and investing in large systems, such as supply chain management, customer relationship management and human resource management. These systems cost tens of thousands of dollars and it will take several years before they are implemented. The process of redesign and employee training, that includes on the job training will exceed the direct costs of IT hardware and software, to which has been described to benefit the organization over many years.

Artificial intelligence, also known as AI, refers to principals and applications of computer algorithms that attempt to imitate various aspect of human intelligence. A1 systems is based on a machine learning methods that is algorithms that gather their own decision making rules from training data by joining large data sets. For example, fraud detection strategies are now developed by machine learning algorithms that analyze millions of historical credit card transactions. Machine learning has increased in production of variety A1 subfields including computer vision, speech recognition, robot control, automated translation between languages, and automated decision making. Over decades A1 systems has produced a number of systems that include the following:

• Intelligent agents, such as Apple’s Siri, Google Now, Microsoft’s Cortana, and Amazon’s Echo. These A1 systems combine speech recognition, background knowledge about the user.

• Self-driving vehicles. Tesla in 2015 released software that allows its customers to put their automobile into self-driving mode on public highways. Uber has recently begun testing this type of cars on the streets of Pittsburg. This demonstrates that computer insight and control and in particular, computer vision and self-steering have reached an important beginning of practical reliability.

• A1 and robotic system that sense and act within the physical world. An example is Nest’s intelligent thermostat, which learns to customize individual buildings their occupants’ routine.

• A1 systems capable of answering many factual questions. IMB’s Watson system defeated the world Jeopardy champion in 2011. Wolfram|Alpha provides a similar broad-scope resource for answering diverse accurate questions.

Tremendous progress has been made in computers, especially in the areas of computer vision and speech recognition. Computer vision is used today in different application such as fingerprint recognition at safety barriers, high speed procession of handwritten address on letter by the U.S. Postal Service, reading of checks deposited at ATMs or via cell phone cameras, and recognition of faces in personal online photo albums.

Settling on a single IT standard may lead to reduced competition and higher prices for customers. (Fuentelsaz et al.2012; Lee and Mendelson 2007). Positive externalities from additional suppliers that join a technological development path support of a standards candidate under development. Direct supply-side network effects are positive externalities generated by the number of suppliers that support a standard of candidates, for example, learning effects, decreased technological uncertainty, the provision of complementary and enabling technologies, and increase legitimacy associated with a technological solution (Arthur 1989; Jacpbsspm amd Bergel 2204; Vam de Vem amd Garud 1989; Wade 1995; Zhao et al. 2011).

While high imitation costs may lead to better IT standards on average, they also lead to a prolonged diversity of standards, which has negative short term welfare effects. As the Bluetooth example illustrates, battles between competing standards candidates may be resolved based on suppliers’ decisions long before any significant competition in the end user market has occurred (Christ and Slowak 2009; Hagiu 2006; Hill 1997).

References:

Information Technology, A. and the U. S. W. (2017). Information Technology and the U.S. Workforce : Where Are We and Where Do We Go From Here? Washington, DC: National Academies Press.
Supply-side network effects and the development of information technology standards; Uotila, Juha; Keil, Thoma; Maula, Markku ; MIS Quarterly Vol. 41.4. pp. 1207-1226/December 2017 

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Artificial Intelligence Paper

The design of the system was the result of a project funded by the Greek Secretariat of Research and Technology . It will operate in the National Center of Immediate Assistance (KEBAB in Greek), which deals with emergency medical incidents by coordinating and routing ambulances to appropriate hospitals and health units as well as offering medical care to patients during their transport to hospitals. Our research unit was mainly responsible for designing the GIS subsystem, which constitutes the primary focus of this paper.

The paper is an extended version of one presented at Delegate 2000 (Drinkers, Gorillas, Makers, Presents, Siestas, & Disallows, 2000). An operation with substantial importance for the handling of emergency incidents is the routing of an ambulance to an incident site and from there to the closest appropriate hospital. The optimal routes correspond to minimum required transportation times. Finding such routes may prove to be time-consuming in the case of large cities such as Athens with very dense road networks.

However, by exploiting recent advances in the field of data structures, the performance of a shortest-path algorithm in terms of the required computational time can be significantly enhanced. The incorporation of the enhanced shortest-path algorithm thin the GIS will lower our system’s response time, thus increasing its inability. This paper is organized as follows. Section 2 briefly presents primary aspects of a Gig’s facilities in modeling and analyzing spatial networks. In Section 3 the overall integrated system is described. Section 4 deals with the GIS subsystem and describes its key functions.

Section 5 briefly explains how the performance of a shortest-path algorithm can be enhanced, while Section 6 demonstrates how to incorporate this enhanced algorithm within a commercial GIS such as Raccoon. Finally, Section 7 summarizes the results of the project. . Modeling and analysis of spatial networks GIS technology integrates common database operations such as query and statistical analysis with the unique visualization and geographic analysis benefits offered by maps (SERIES Web site; Franklin, 1992; Mueller, 1993). Among other things, a GIS facilitates the modeling of spatial networks (e. . Road networks), Other partners in this project were the University of Piraeus, the National Technical University of Athens, the Aristotle University of Thessalonians and the companies ITCH G. Drinkers et al. / Compute. , Environ. And Urban Systems 25 (2001) 267-278 69 offering algorithms to query and analyze them. Spatial networks are modeled with graphs. In the case of road networks, the graph’s arcs correspond to street segments whereas the nodes correspond to street segment intersections. Each arc has a weight associated with it, representing the impedance (cost) of traversing it.

In most cases, an arc’s impedance is a function of the corresponding street segment’s length and traffic volume. A GIS usually provides a number of tools for the analysis of spatial networks. It generally offers tools to find the shortest or minimum impedance route wrought a network and heuristic procedures to find the most efficient route to a series of locations, commonly called the traveling salesman problem. Allocation functions assign portions of the network to a resource supply location and tracing tools provide a means to determine whether one location in a network is connected to another.

Distance matrix calculation can be used to calculate distances between sets of origins and destinations whereas location-allocation functions determine site locations and assign demand to sites. Moreover, street addresses can be converted to map coordinates (address coding). Finally, dynamic segmentation operations offer ways of modeling events (e. G. Pavement quality, speed zones) along routes (SERIES Web site). These capabilities of GIS for analyzing spatial networks enable them to be used as decision support systems for the districting and routing of vehicles (Grassland, Wynn, & Perkins, 1995; Keenan, 1996, 1998). . The overall integrated system Up till now, Kebab’s employees were using paper maps and their own experience in order to achieve the effective routing and districting of ambulances. However, these two functions, which constitute significant areas in the field of decision support yeasts (Eom, Lee, & Kim, 1993), require the integration of a computer-based system with geographic analysis and visualization tools and a telecommunication network. The operation of the integrated system will automate and enhance many of Kebab’s services. The system’s architecture is depicted in Fig. 1.

It is based on the integration of GIS, GASP and GSM technologies. The GASP and GSM technologies will be used to transmit the exact positions of ambulances to the GIS operating in Kebab’s Operations Center. The integration of these technologies enables the management of vehicles such as many trucks, patrol cars and ambulances (Hauberk, 1995). All these applications are parts of the new emerging disciplines of teleprocessing and telecommunication (Laurie, 1999, 2000; Tanzania, 2000). Each ambulance will be equipped with a GASP receiver to determine its exact position based on the signal transmitted by satellites.

In addition, it will have a GSM modem in order to transmit its position to the base station in the Operations Center. This will be achieved through the GSM network. Furthermore, through the GSM network other be equipped with a computer or a 270 Fig. 1 . The overall integrated system. Mobile data terminal to display the route computed by the GIS operating in the Operations Center. Kebab’s Operations Center will exchange data with the ambulances through the GSM network. It will receive the ambulance positions and will use the GIS to perform the functions described in Section 4.

The optimal route calculated for a specific ambulance will be transmitted to it. In the Operations Center there will be a computer dedicated to communication with the ambulances and another one for the operation of the GIS. In addition, there will be one or more computers for the operation of the database management system (DB’S) containing data used by the GIS. Nowadays, most GIS software packages offer a rich set of tools and extensions, enabling the incorporation of GASP data and offering real-time tracking capabilities.

Archive, for instance, offers an extension called Tracking Analyst that allows direct feed and playback of real-time data within the Archive GIS environment (SERIES Web site). The system’s architecture follows the centralized approach (Laurie, 2000; Tanzania, 2000) whereby a control center (in our case Kebab’s Operations Center) coordinates the fleet of mobile vehicles. Data from the vehicles and sensors are sent to this center and, after being evaluated, data and instructions are transmitted to the vehicles. A strong point of this architecture is the easiness with which it is designed. 71 However, the danger of a crash in the control center constitutes a major weakness (Lament, 2000; -rant, 2000). 4. The GIS subsystem The GIS will make use of various data that are either stored in spatial databases and DB’S or transmitted through the GSM network. Spatial data will cover the road network, the locations of hospitals and medical centers, the positions of ambulances, he distribution of incidents occurring in the past, the distribution of population characteristics (e. G. Demographic characteristics or disease spreading), and locations of various landmarks.

Basic spatial data for the road network relate to intersections and the road segments are coded based on intersection type (e. G. Railroad crossing, street intersection) and the type of traffic control device present (e. G. Stop sign, stop light). Road segments form the framework for a number of other geographic features defined using route systems. Street names, for instance, are defined as routes. Along them speed zones ND speed limit signs are recorded as linear and point events, respectively. In addition, lanes are recorded as linear events along these routes.

Since the majority of streets are only two-lane residential streets, only sections with more than two lanes are recorded. Another important aspect is the recording of the locations of hospitals and gas stations. Moreover, address information related to the road network is being stored, facilitating coding operations. Data concerning road traffic will be very useful for the routing of ambulances. These data will be updated by processing traffic statistics and simultaneously taking into inconsideration online data deriving from traffic sensors installed on the road network.

The National Technical University of Athens has installed loop sensors on the road network of Athens, providing essential information on traffic conditions. Traffic data will be stored in a DB’S. Data pertaining to events such as road works or demonstrations that also affect road traffic will be made available from the municipality or the police. Data concerning hospitals, ambulances, and their personnel will also be stored in the DB’S and used by the GIS whenever it is necessary. Information linking conventional loophole numbers with addresses is also stored in a DB’S.

Its importance will become evident in the next section. Some of the primary functions performed by the GIS operating in KEBAB will be the following:  Depiction on a map of ambulance positions and hospital locations. Useful queries that will be performed include the display of information about an ambulance or a hospital chosen from the map, locating all ambulances positioned within a block, all ambulances that are closer to a hospital or some other spot, etc. Different symbols will be used for displaying an ambulance, 272.  Pending on its status: an ambulance may be standing by, handling an incident, or tools of the GIS will take into consideration the data concerning the road network, past incident distribution, population distribution, hospital locations, locations of gas stations and traffic conditions and will propose efficient distributions of ambulances. A variety of criteria should be considered in order to perform this operation. For example, areas where many incidents take place should be allocated more ambulances. A densely populated area entails a higher probability of an incident occurring.

Additionally, an area’s urban planning affects the way incidents are handled. Areas close to major streets facilitate ambulance access to whereas areas with narrow streets inhibit it. If the administrator of the GIS chooses to distribute ambulances according to his/her own criteria, the depiction on the map of all the available information and the interaction with the GIS will be of significant assistance. Finding the site of the incident. Based on the address given by the person calling Kebab’s Operations Center for help, the GIS can use address coding functions to find the incident’s coordinates on the map.

However, in many cases the person calling for help may be at a loss for words and thus unable to give precise information about the site of the incident. Therefore, the system should include a mechanism for matching a call to an address. The DB’S linking conventional telephone numbers with addresses will facilitate this matching. Things are more complicated if the call is made from a cellular phone, however. In this case, the assistance of the cellular phone providers will be required in order to match a caller’s location to the closest address or landmark. Choosing the appropriate ambulance to Andre an emergency incident.

According to ambulance positions, the type and location of the incident and traffic conditions, the GIS finds the ambulance requiring the least time to reach the site of the incident. The choice of ambulance depends on the type of incident because some ambulances are equipped to handle special emergency cases. Routing an ambulance to the incident site and from there to the closest appropriate hospital. The GIS will be used to find the optimal routes corresponding to minimum required transportation time. The distance as well as traffic data will be taken into account. The appropriate hospital will furthermore depend on the type of incident.

Such information will be derived from communication through the GSM network between the ambulance personnel and the personnel in the Operations Center. The GIS can also present the driver with directions corresponding to the routes generated (e. G. Go straight ahead, turn right to Armor Street, etc. ). These directions will be transmitted to the ambulance. In a real- time system like ours, the time performance of the routing function is of vital significance. Generation of statistics regarding incidents. The GIS, in cooperation with the DB’S annotating incident records, can significantly assist in the statistical analysis of incidents.

Consequently, important conclusions supporting the ambulance districting can be obtained. The most efficient implementation of Disaster’s algorithm An operation with substantial importance for the handling of emergency incidents is the routing of an ambulance to an incident site and from there to the closest appropriate hospital. The optimal routes correspond to minimum required transportation times. Finding such routes may prove to be time-consuming in the case of large cities such as Athens with very dense road networks. A real-time system however, must be able to give a prompt reply to such queries.

Disaster’s algorithm is a simple and consequently easily implemented algorithm for finding shortest routes and is the most widely used in GIS software packages. Its performance depends on the data structures (e. G. Heaps or priority queues) used to implement the graph representing the spatial network. By exploiting recent advances in the field of data structures the performance of a significantly enhanced. We assume that we are given a graph with n nodes, m arcs, and integral arc lengths in the range , where C is the largest arc length. This graph represents the road network.

Boris V. Characters, Andrew V. Goldberg and Craig Silversides developed the hot queue data structure (Characters, Goldberg, & Silversides, 1996, 1999) that combines the best features of heaps and multi-level buckets (Denary & Fox, 1979) in a natural way. They proved in theory that if C is very small compared to n, the data structure performs as a multi-level bucket structure. If C is very large, the data structure performs as the heap used in it. For intermediate values of C, the data structure performs better than either the heap or the multi-level jacket structure.

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Artificial Intelligence Critique Essay

One were to take a look around the room they are currently in, chances are there would be some form of artificial Intelligence present. From cell phones to computers – artificial Intelligence Is everywhere and even a way of life. The next generation of people may never know what life is without some form of […]

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Artificial Intelligence and Learning Computers

Artificial Intelligence & Learning Computers Presented by: S. DEEPAKKUMAR Abstract The term artificial intelligence is used to describe a property of machines or programs: the intelligence that the system demonstrates. Among the traits that researchers hope machines will exhibit are reasoning, knowledge, planning, learning, communication, perception and the ability to move and manipulate objects. Constructing […]

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Artificial Intelligence and the Modern Military

Artificial Intelligence and the Modern Military Wayne K Sullivan Saint Leo University MGT 327, CA01, Management Information Systems Professor Lawrence Mister November 26, 2011 Purpose: In today’s military, leaders are continuously seeking ways to incorporate new technology to take the place of human soldiers. It has long been an important goal to be able to […]

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Elon Musk: Artificial Intelligence Is Humanity’s ‘Biggest Existential Threat’

Elon Musk, the CEO of Tesla and SpaceX, has made his deep reservations about artificial intelligence abundently clear: In June, , he told CNBC “nobody expects the Spanish Inquisition, but you have to be careful,” and in August he that AI’s applications could be “potentially more dangerous than nukes” via a tweet. Speaking at a […]

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